Results 91 to 100 of about 9,194 (254)

MONEY LAUNDERING DETECTION USING GRAPH NEURAL NETWORKS ENHANCED WITH AUTOENCODER COMPONENTS

Studia Universitatis Babes-Bolyai: Series Informatica
The paper addresses the topic of detecting money laundering operations in transaction data represented as graph data-structures. We propose the integration of autoencoder components in Graph Neural Networks (GNN) architectures, in order to incorporate a
Tudor-Ionuț GRAMA
doaj   +1 more source

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

Prediction of microbe–drug associations based on a modified graph attention variational autoencoder and random forest

Frontiers in Microbiology
IntroductionThe identification of microbe–drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational ...
Bo Wang, Bo Wang, Fangjian Ma, Xiaoxin Du, Guangda Zhang, Jingyou Li, Jingyou Li   +6 more
doaj   +1 more source

Robust Representation Learning for Clean Feature Discovery in Incomplete Multi‐View Clustering

Advanced Intelligent Systems, EarlyView.
Robust feature discovery in incomplete multi‐view clustering is achieved by coupling RPCA‐based clean representation recovery with neural‐network‐assisted graph learning. The resulting RIMVC framework constructs cleaner and more discriminative graph‐structured representations from incomplete and noisy multi‐view data, improving clustering robustness ...
Ping Hu, Chenggang Lu, Rui Zhang, Haijun Yan, Yesong Xu   +4 more
wiley   +1 more source

Guided graph compression for quantum graph neural networks

Machine Learning: Science and Technology
Graph neural networks (GNNs) are effective for processing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix operations on GPUs. Quantum computing offers a promising avenue to address
Mikel Casals, Vasilis Belis, Elias F Combarro, Eduard Alarcón, Sofia Vallecorsa, Michele Grossi   +5 more
doaj   +1 more source

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays

Machines
Solar energy is a critical renewable energy source, with solar arrays or photovoltaic systems widely used to convert solar energy into electrical energy. However, solar array systems can develop faults and may exhibit poor performance.
Murshedul Arifeen, Andrei Petrovski, Md Junayed Hasan, Khandaker Noman, Wasib Ul Navid, Auwal Haruna   +5 more
doaj   +1 more source

GRAPH AUTOENCODERS: A SURVEY

World Journal of Engineering Research
Graph analysis serves as a robust approach for the in-depth exploration of the inherent characteristics of graph data. Nonetheless, due to the non-Euclidean nature of such data, conventional data analysis techniques often incur significant computational expenses and spatial overhead.
openaire   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Inherent-attribute-aware dual-graph autoencoder for rating prediction

Journal of Information and Intelligence
Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users' preferences.
Yangtao Zhou, Qingshan Li, Hua Chu, Jianan Li, Lejia Yang, Biaobiao Wei, Luqiao Wang, Wanqiang Yang   +7 more
doaj   +1 more source

Nanostructured Deep Eutectic Systems in Healthcare: From Bioactive Solvents to Intelligent Biointerfaces, Wearables, and AI‐Driven Design

Advanced NanoBiomed Research, EarlyView.
Beyond the green solvent paradigm, this review redefines Deep Eutectic Systems (DES) as programmable supramolecular nanoassemblies. We survey their biomedical convergence: stabilizing thermolabile mRNA to enable cold chain‐free logistics, reshaping transdermal microneedle delivery, enabling long‐term wearables via eutectogels, and utilizing Generative ...
Jeesu Moon, Min Seo Kim, Jae‐Seung Lee
wiley   +1 more source